Finally, generative models can also be used to produce additional training data. In [1]}, the authors used a generative adversarial network (GAN) to boost their measured dataset by generating additional channel state information (CSI, [2]}) amplitude maps of an indoor environment. They found that the classification accuracy of their network significantly improved. Likewise, in [3]}, the authors used GANs for data recovery. Their goal was user tracking, done by measuring successive locations of a moving user. In cases where no measurements were collected (eg. not reachable from an access point (AP)), the GAN was used to estimate the user's location.
